Enhancing regulatory compliance in AEC industry via LLM-powered decision frameworks

Despite the critical role of boards and their significant interest in Diversity, Equity, and Inclusion, there is a scarcity of studies on board diversity within the architecture, engineering, and construction (AEC) industry. This paper aims to investigate board gender diversity in the AEC industry and provide a comprehensive overview of its progression over time, comparing it with non-AEC industry. The findings reveal that, on average, the AEC industry exhibits lower board gender diversity compared with non-AEC industry, although the gap between the two industries has significantly narrowed over time. The professional characteristics of female directors suggest that board gender diversity in the AEC industry seems to not yet fully embrace an inclusive and equitable work environment. Notably, female directors in the AEC industry have a shorter tenure, hold a higher number of outside board positions, display a relatively worse board meeting attendance rate, and tend to concentrate on professional positions related to finance, in comparison with male directors in the same industry or female directors in non-AEC industry. This paper contributes to the literature by shedding light on the specific dynamics and challenges related to board gender diversity in the AEC industry using comprehensive and up-to-date data.

Purpose In recent decades, interest in digital transformation (DX) within the architecture, engineering, and construction (AEC) industry has significantly increased. Despite the existence of several literature reviews on DX research, there remains a notable lack of systematic quantitative and visual investigations into the structure and evolution of this field. This study aims to address this gap by uncovering the current state, key topics, keywords, and emerging areas in DX research specific to the AEC sector. Design/methodology/approach Employing a holistic review approach, this study undertook a thorough and systematic analysis of the literature concerning DX in the AEC industry. Utilizing a bibliometric analysis, 3,656 papers were retrieved from the Web of Science spanning the years 1990–2023. A scientometric analysis was then applied to these publications to discern patterns in publication years, geographical distribution, journals, authors, citations, and keywords. Findings The findings identify China, the USA, and England as the leading contributors in the field of DX in AEC sector. Prominent keywords include “building information modeling”, “design”, “system”, “framework”, “adoption”, “model”, “safety”, “internet of things”, and “innovation”. Emerging areas of interest are “deep learning”, “embodied energy”, and “machine learning”. A cluster analysis of keywords reveals key research themes such as “deep learning”, “smart buildings”, “virtual reality”, “augmented reality”, “smart contracts”, “sustainable development”, “building information modeling”, “big data”, and “3D printing”. Originality/value This study is among the earliest to provide a comprehensive scientometric mapping of the DX field. The findings presented here have significant implications for both industry practitioners and the scientific community, offering a thorough overview of the current state, prominent keywords, topics, and emerging areas within DX in the AEC industry. Additionally, this research serves as an invaluable reference and guideline for scholars interested in this subject.

The Architecture, Engineering, and Construction (AEC) industry is plagued by persistent challenges such as low productivity, cost overruns, and frequent project delays. Integrated Project Delivery (IPD) has emerged as a potential solution, offering collaborative approaches to improve project outcomes. This study proposes a two-tiered methodology for evaluating the disruptiveness of innovations in the AEC industry, with a particular focus on IPD as a disruptive innovation. In the first tier, a multidimensional framework is developed to systematically assess the disruptiveness of innovations in the AEC sector. This framework, informed by a thorough literature review and disruptive innovation theory, includes dimensions such as business models, processes, and anticipated outcomes. The second tier applies the framework by analyzing the disruptiveness of IPD. The assessment draws on data from three comprehensive studies, including ethnographic research, interviews, and focus groups, which examine IPD’s impact on different stakeholder groups such as clients, consultants, and contractors. Findings reveal that IPD has the potential to significantly disrupt traditional business models, processes, and project outcomes, particularly at the project level. Notable disruptive characteristics include shifts in collaboration dynamics, redefined project financing models, and improved efficiency. However, several barriers hinder IPD adoption, including resistance to change and misalignment with conventional contractual structures. Expert interviews support these results, indicating that IPD represents a fundamental shift in the AEC industry. This research contributes to the existing body of knowledge by offering a structured framework for assessing the disruptiveness of AEC innovations and demonstrating its practical application. In this way, AEC organizations, projects, and practitioners can better strategize for the adoption of any new disruptive innovation and thus pursue a strategic advantage in the highly competitive industry market.

<p>Advancements put forth by the Structural Engineering Institute (SEI) of ASCE are paving the way for a regulated engineered alternative to the long‐standing archaic requirements for structural fire design. For the past century, project stakeholders have tolerated a strikingly inefficient and amorphous system for protecting structures from uncontrolled fire. Traditionally, fire protection is prescribed for structures after they have been optimized for ambient design loads (i.e., gravity, wind, seismic, and others), with no explicit consideration of structural fire performance. Accordingly, the vulnerability of buildings to structural failure from uncontrolled fire is presumably variable across different jurisdictions, which have varying structural design requirements for ambient loads. Also, structural engineers are often absent from the structural fire protection design process entirely.</p><p>In conjunction with new provisions in Appendix E of Minimum Design Loads and Associated Criterial for Buildings and Other Structures (ASCE/SEI 7‐16), a first‐of‐its‐kind ASCE/SEI Manual of Practice 138: Structural Fire Engineering, has been developed to provide structural engineers a baseline level of guidance to practice structural fire engineering. Also, ASCE/SEI has partnered with the Charles Pankow Foundation to conduct an ambitious project meant to showcase this new technology to the industry. Advancing the adoption of performance‐based structural fire engineering within the AEC industry will benefit public safety while delivering more efficient and economic building designs.</p>

The implementation of artificial intelligence (AI) and machine learning (ML) in the architecture, engineering, and construction (AEC) domain has gained significant attention within academic research and construction technology companies. The scientific field often relies on information collected from the industry for theoretical development and sometimes test cases. However, to the best of the authors’ knowledge, there has been no systematic effort to compare academic and industry trends and developments. In this work, we examine the development and application of ML in the AEC domain from both academic and industry perspectives. To investigate both perspectives, we implement a mixed-methods approach including an academic literature review, a web-based mapping of construction technology companies worldwide, and an online questionnaire targeting practitioners and technology companies. Overall, our mixed methods analysis reveals a strong alignment between academic research and industry practice in targeting early design and post construction phases, with both communities prioritizing the application of ML for energy efficiency, facility management, and site safety, particularly within the context of Building Information Modeling (BIM). However, industry devotes more effort to sustainability, cost and scheduling solutions, whereas academia lags in these areas and often relies on limited or synthetic datasets. Finally, both sectors identify data availability and quality, particularly the scarcity of large, labeled, domain-specific repositories, as the primary barrier to wider ML adoption in the AEC industry.

In Canada, the Architecture, Engineering, and Construction (AEC) industry generates approximately 3.4 million tons of Construction, Demolition, and Renovation Waste (CDRW) annually, presenting a critical environmental challenge. While global studies highlight the potential of Circular Economy (CE) principles to address this issue, their application in Canada’s AEC industry remains limited due to fragmented information, which hinders understanding and execution of CE principles. The study aims to assess the state of CE strategies in Canada’s AEC industry via a literature review. It identifies key challenges and opportunities and offers insights to enhance the integration of CE principles within the Canadian context. These are categorized into four key areas: technical, economic, socio-cultural, and governmental. Finally, the execution status of each opportunity within Canada is evaluated and classified as either already executed, in transition, or yet to be executed. The findings reveal 16 critical opportunities with the potential to enhance circularity within Canada’s AEC sector, with 25% yet to be executed, 62% in transition, and 13% already executed. These insights underscore the potential for technological innovation, socio-cultural shifts, and regulatory reforms. This research provides strategic guidance for policymakers, industry professionals, and stakeholders, offering a roadmap for Canada’s AEC industry.

One of a kind products, processes, and partner groups typically characterise the architecture, engineering, and construction (AEC) industry. This has led to a challenging industry environment with islands of automation, difficult communication between stakeholders, and increasing specialisation. Building information modelling (BIM) is a modern approach to building design, documentation, delivery, and lifecycle management that addresses many AEC challenges. The outcome of this process is a comprehensive building information model that encompasses all relevant data and information related to the construction process as well as the built asset. This model is a primary resource for diverse end-user applications, including augmented reality, that could be essential in bridging the gap between the virtual and physical worlds and has become a component of modern digital workflows within the AEC industry. This chapter provides an overview of AEC specifics and how those reflect in implementing augmented reality in the context of BIM processes. It also delves into the challenges that must be addressed to facilitate the widespread adoption of augmented reality technology in the AEC sector.

The aim of this study is to identify the advantages and limitations in the implementation of the Building Information Modelling (BIM) and Integrated Project Delivery (IPD) in the UK consulting sector. A literature review critically analyses the existing literature, (including online articles and publications owing to the current and fast-moving nature of the topic in question). Next, a qualitative research approach was employed through interviews which targeted BIM professionals across the AEC sector in the UK. Barriers to BIM were found to include lack of enthusiasm for potential opportunities for BIM and IPD. Cultural factors which act against BIM and IPD implementation include, willingness to adopt BIM environment, software cost, high-end user maintenance cost. Furthermore, a need was determined for UK government support and encouragement for the use of BIM and IPD for smaller projects. This study recommends that schools take steps to improve career advice and guidance for students in relation to construction, specifically increasing awareness of the opportunities available related to BIM and working professionals to increase awareness of and employment in BIM through training and apprentices as appropriately. Finally, the paper concludes that BIM and IPD should be more widely adopted within AEC industries in the UK to maximise benefits from both systems.

This study investigates the current landscape of generative AI and LLM applications in architecture, engineering, and construction (AEC), focusing on trends, practical implications, educational strategies, and imperatives for upskilling. Employing a six-stage systematic review sourced from Google Scholar, Scopus and Web of Science, 120 papers were analyzed to provide a comprehensive understanding of the role of these technologies in shaping the future of the AEC industry. By addressing these objectives, the research contributes to enhancing knowledge about the potential impacts of generative AI and LLMs on the AEC industry and provides insights into strategies for leveraging these technologies effectively. This study underscores the transformative impact of AI and advanced technologies on the AEC sector and education. By enhancing learning experiences and optimizing construction processes, AI fosters personalized education and efficient project management. The study’s significance lies in its identification of necessary skills and competencies for professionals, ensuring effective AI integration. Implications include the need for continuous professional development, formal education, and practical training to leverage AI’s potential fully. This paves the way for sustainable, intelligent infrastructure and accessible, adaptive learning environments, driving innovation and efficiency in both fields.

In the era of digital transformation, the role of artificial intelligence (AI) is increasingly prevalent in the Architecture, Engineering, and Construction (AEC) sector. With the emergence of AI ca-pable of text generation, the AEC industry is actively seeking opportunities for its application, leading to the development of specialised software for various tasks to facilitate automation. Following the literature, this article presents the evolution of artificial intelligence and its key characteristics. Furthermore, the study explores the potential applications of AI in the AEC sec-tor, categorising different solutions based on their intended use and delving into their applica-bility, usability, and key values. The article presents solutions available in text generation, image creation, design optimisation, sustainability and simulation, and within the field of Building In-formation Modeling (BIM). In addition to these possibilities, the article focuses specifically on analytical solutions and provides a more detailed insight into their functioning. After outlining the possibilities, it discusses the integration of AI into the design process. The article highlights notable examples of the rising popularity of AI in the construction industry and discusses their utility in various stages of the design process.

In the context of “Industry 4.0,” the construction industry is undergoing a continuous process of digital transformation. In recent years, with the rapid development of artificial intelligence (AI) technology, AI has been successfully applied across various domains within the construction sector. This paper conducts a comprehensive review based on scientific bibliometric analysis and systematic evaluation to provide an overview of AI applications and developments in the architecture, engineering, and construction (AEC) industry, as well as to discuss future research trends. Firstly, a bibliometric analysis was carried out on 3,410 journal articles published between 2003 and 2022. This analysis examined characteristics such as annual publication trends, key researchers and their affiliations, countries of origin, collaborative relationships, prominent journals, and keyword clustering. Subsequently, through systematic review, three critical themes in the field were identified and analyzed: performance prediction, intelligent optimization, and engineering safety and risk. To gain a deeper understanding of cutting-edge AI technologies and their potential applications in the AEC sector, potential future research directions were discussed, including digital twins, blockchain, augmented reality, and virtual reality, all aimed at driving the intelligent and digital transformation of the AEC industry.

Purpose The Internet of Things (IoT) offers substantial potential for improving efficiency and effectiveness in various applications, notably within the domain of smart construction. Despite its growing adoption within the Architecture, Engineering, and Construction (AEC) industry, its utilization remains limited. Despite efforts made by policymakers, the shift from traditional construction practices to smart construction poses significant challenges. Consequently, this study aims to explore, compare, and prioritize the determinants that impact the acceptance of the IoT among construction practitioners. Design/methodology/approach Based on the integrated model of Unified Theory of Acceptance and Use of Technology (UTAUT2), Task-Technology Fit (TTF), and perceived risk. A cross-sectional survey was administered to 309 construction practitioners in China, and the collected data were analyzed using structural equation modeling (SEM) to test the proposed hypotheses. Findings The findings indicate that TTF, performance expectancy, effort expectancy, hedonic motivation, facilitating conditions, and perceived risk exert significant influence on construction practitioners’ intention to adopt IoT. Conversely, social influence and habit exhibit no significant impact. Notably, the results unveil the moderating influence of gender on key relationships – specifically, performance expectancy, hedonic motivation, and habit – in relation to the behavioral intention to adopt IoT among construction practitioners. In general, the model explains 71% of the variance in the behavioral intention to adopt IoT, indicating that the independent constructs influenced 71% of practitioners’ intentions to use IoT. Practical implications These findings provide both theoretical support and empirical evidence, offering valuable insights for stakeholders aiming to gain a deeper understanding of the critical factors influencing practitioners’ intention to adopt IoT. This knowledge equips them to formulate programs and strategies for promoting effective IoT implementation within the AEC field. Originality/value This study contributes to the existing literature by affirming antecedents and uncovering moderators in IoT adoption. It enhances the existing theoretical frameworks by integrating UTAUT2, TTF, and perceived risk, thereby making a substantial contribution to the advancement of technology adoption research in the AEC sector.

The architecture, engineering, and construction (AEC) industry faces inefficiencies stemming from inadequate knowledge sharing and limited implementation of best practices. This study examines the feasibility of developing an open repository to facilitate widespread dissemination of best practices within the AEC sector. Using interviews (N = 16), a Delphi study (Round 1: N = 21, Round 2: N = 12), focus groups (Session 1: N = 10, Session 2: N = 7), and steering committee feedback (N = 10), this study reveals a lack of consensus on defining best practices, variability in internal knowledge management systems, and the potential benefits of leveraging technology for knowledge cataloging and sharing. The authors proposed a ten-step framework for creating an open repository to address these challenges, focusing on industry sectors with substantial market potential, a standardized submission template, and user communication tools. The successful implementation of an industry-wide open repository has the potential to break down knowledge silos, catalyze innovation, and foster continuous improvement across AEC organizations. However, critical challenges persist, such as securing long-term funding, developing sustainable governance models, and creating incentive structures to encourage participation. Future research should investigate these aspects and examine real-world applications of such platforms to assess their impact on project outcomes within the AEC industry.

The Architecture, Engineering, and Construction (AEC) industry plays a crucial role in shaping the built environment but contributes significantly to environmental impact, accounting for nearly 40% of global energy-related carbon dioxide emissions. An interdisciplinary systematic literature review (SLR) explores how transdisciplinary education (TDE) can be integrated into AEC curricula to address environmental sustainability challenges. TDE facilitates students’ understanding of the environmental impact (EI) of their design projects and equips them with the knowledge and skills to develop sustainable solutions. This review examines various pedagogical models, including problem-based learning, to evaluate their effectiveness in fostering TDE for reducing EI in the AEC sector. Findings from the literature are synthesized to present a framework aligned with sustainable development goals (SDGs) that promotes sustainability in AEC education. The review aims to inform future research and curriculum development, advancing the role of education in equipping future professionals to mitigate environmental impacts in the AEC industry.

The Architecture, Engineering, and Construction (AEC) industry is witnessing a paradigm shift with the integration of computing technologies. This study provides a comprehensive exploration of the historical development and transformative benefits associated with the computational co-design on Omniverse in the AEC sector. Historically, the AEC industry has relied on diverse software solutions for design, collaboration, and project management. The emergence of Omniverse, a real-time 3D simulation and collaboration platform, presents an innovative avenue to streamline workflows and enhance cross-disciplinary collaboration. To illustrate the practical implementation of computational co-design, a case study is performed in this research demonstrating how these technologies can potentially be leveraged to enhance design iteration speed, foster multidisciplinary team collaboration, and optimize project outcomes in real-time simulations for visualization. The findings from this case study are expected to provide valuable insights into the tangible benefits and potential challenges of incorporating computational co-design into AEC workflows.